Summary The latent period between brain injury and subsequent epilepsy is months to years in duration, providing a unique window for antiepileptogenic therapy. Recent reports of promising experimental disease-modifying therapies can't advance to clinical trials until three hurdles are overcome: First, quantification of epilepsy is necessary to assess efficacy of interventions, but epilepsy after brain injury is very difficult to quantify: the latency to first seizure is long and variable, and early seizures are often subtle, infrequent, and clustered between long inter-cluster intervals. Second, because of the long latency between injury and epilepsy, clinical trials need to be quite prolonged and therefore prohibitively expensive. Third, because ~ 20% of moderately brain-injured patients develop epilepsy, most of the treated patients could not benefit from long-term antiepileptogenic therapy, despite exposure to the risks and side effects. These three hurdles could be overcome with sufficiently accurate biomarkers. We recently demonstrated that early electrographic epileptiform activity is a promising predictor of epilepsy after brain injury induced by kainic acid. Here we propose to address critical knowledge gaps regarding electrographic biomarkers of epileptogenesis. The predictive power of electrographic biomarkers has not been assessed after more clinically relevant injuries such as trauma and hypoxic-ischemic injury. The predictive power of electrographic biomarkers has not been systematically compared to the predictive power of traditional physical descriptors of injury, such as lesion size and location. Furthermore, it has not been determined whether combining electrographic and physical-injury parameters would improve their predictive power. We will employ well-established models of clinical injuries, the lateral fluid percussion (LFP) trauma model and the Rice- Vannucci model of focal hypoxia-ischemia in P30 rats. The incidence of epilepsy in these models is close to the human experience, and thus provides a more rigorous test of the predictive power of these biomarkers than the kainate model. Further, the latency to seizures is sufficiently long in these models to enable testing as to whether the appearance of early electrographic biomarkers is more closely related to the time elapsed since the injury, or to the time remaining prior to the first seizure; the nature of these relationships will significantly impact the design of clinical studies of these biomarkers. In Aim 1, we will use a novel miniature telemetry device for continuous recording of video-EEG together with validated, unbiased computer detection algorithms to quantify early epileptiform activity and seizures in these brain injury models. We will optimize EEG sampling and develop the best predictive model based on epileptiform electrographic activity and injury descriptors, and then prospectively test this model in a second group of animals. In Aim 2, we will use the same approach to test whether early electrographic epileptiform activity and injury descriptors predict the severity of epilepsy, including latency to first seizure and seizure frequency, once epilepsy is fully developed.